Systems and methods for a power efficient method for detecting wear and non-wear of a sensor

ABSTRACT

The present solution provides a system and a method for determining the wear compliance of a wearable sensor. More particularly, the systems and methods described herein enable the power-efficient and accurate determination of a sensor&#39;s wear and non-wear state. Accurately, detecting the wear and non-wear state ensures the correct and accurate measurement of behavioral signals. Additionally, the systems and methods enable caretakers to remotely monitor patient wear compliance and behavioral parameters.

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the file or records of the Patent and Trademark Office, but otherwise reserves all copyright rights whatsoever.

BACKGROUND OF THE DISCLOSURE

Medical alert devices are often marketed to the elderly as a means to stay in contact with caretakers and emergency services. Some devices also enable the measurement of behavioral and environmental parameters. Unfortunately, many patients neglect to wear their medical alert devices, resulting in a disconnect between the patient and emergency services. Additionally, behavioral and environmental parameters collected by the device during a non-wear state can prove inaccurate. To date the devices cannot determine their wear state in a power-efficient and accurate method.

BRIEF SUMMARY OF THE DISCLOSURE

The present solution provides a system and a method for determining the wear compliance of a wearable sensor. More particularly, the systems and methods described herein enable the power-efficient and accurate determination of a sensor's wear and non-wear state. Accurately, detecting the wear and non-wear state ensures the correct and accurate measurement of behavioral signals. Additionally, the systems and methods enable caretakers to remotely monitor patient wear compliance and behavioral parameters.

According to one aspect of the disclosure, a method for determining if a wearable device is worn by a wearer includes sampling for a predetermined time step a signal of a motion sensor within the wearable device. The signal indicates a motion of the wearable device. The method also includes determining an orientation of the wearable device from the signal and detecting a movement of the wearable device based on a difference between the orientation and at least one previously determined orientation. The method further includes storing, into an array of flags, a flag that indicates the detection of movement of the wearable device. Each flag in the array of flags indicates whether there has been a detection of movement of the wearable device for each of the at least one previously predetermined time steps. The method also includes determining that the wearer is wearing the wearable device at the predetermined time step based on identifying a predetermined number of flags in the array of flags.

In some implementations, determining that the wearer is wearing the wearable device is responsive to identifying a continuous subset of flags in the array of flags that all indicate detection of movement.

In other implementations, detecting the movement of the wearable device further includes generating an intensity value for the predetermined time step by summing a plurality of samples of the signal over the predetermined time step. Detecting the movement also includes determining if the intensity value is above a first threshold.

In certain embodiments, summing the samples of the movement signal further includes summing the absolute value of the differences between adjacent samples in the plurality of samples. Summing the samples can include summing the absolute value of each of the plurality of samples minus a running mean.

In yet other implementations, sampling includes sampling only one sample during the predetermined time step. In other implementations, sampling includes sampling for only a subset of time within the predetermined time step. In some implementations, determining that a movement occurred can also include counting a number of flags in the array of flags that have indicate detection of movement, and determining if the number of flags is above a second threshold.

In some implementations, a band-pass filter, low pass or a high pass filter is applied to the signal. The motion sensor can be one of an accelerometer, a gyroscope or a passive motion sensor. The orientation can include one of the motion sensor's pitch, roll or yaw.

In certain implementations, the first orientation is determined by filtering the signal and calculating the difference between the x, y, and z axis. In some implementations, the determination that the wearer is wearing the wearable device is made by a second device in communication with the wearable device.

According to another aspect of the disclosure, a wearable device includes a processor coupled to a motion sensor. The processor is configured to sample from the motion sensor for a predetermined time step and determine an orientation of the wearable device from the signal. The device is also configured to detect a movement of the wearable device based on a difference between the first orientation and at least one previously determined orientation. The processor also stores a flag that indicates the detection of movement of the wearable device, wherein each flag in the array of flags indicates whether there has been detection of movement of the wearable device for each of the at least one previously predetermined time steps. The processor also determines that the wearer is wearing the wearable device at the predetermined time step based on identifying a predetermined number of flags in the array of flags.

In some implementations, the processor is further configured to determine that the user is wearing the wearable responsive to identifying a continuous subset of flags in the array of flags that all indicate detection of movement. The processor can also generate an intensity value for the time step by summing a plurality of samples of the signal over the predetermined time step, and determine if the intensity value is above a first threshold.

In certain implementations, processor is further configured to sum the samples of the signal by summing the absolute value of the differences between adjacent samples in the plurality of samples. In some embodiments, the processor is configured to sample only one sample during the predetermined time step.

In some implementations, the processor determines that the user is wearing the wearable device by counting a number of flags in the array of flags that indicate detection of movement and determine if the number of flags is above a second threshold.

In some implementations, the device includes a filter module to apply one of a band-pass filter, low pass, or a high pass filter to the signal of the motion sensor. The motion sensor can include one of an accelerometer, a gyroscope or a passive motion sensor.

In other implementations, the processor is configured to sample the signal with the motion sensor for only a subset of time within the predetermined time step. The motion sensor can be configured to measure at least two of a pitch, a roll, or a yaw.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the following description taken in conjunction with the accompanying drawings, in which:

FIG. 1A is a block diagram of an embodiment of a wearable sensor.

FIG. 1B is a block diagram of an embodiment of a base station.

FIG. 1C is a schematic of an environment including the wearable sensor of FIG. 1A and base station of FIG. 1B.

FIG. 1D is a block diagram depicting an embodiment of a network environment comprising client device in communication with server device;

FIG. 1E is a block diagram depicting a cloud computing environment comprising client device in communication with cloud service providers;

FIGS. 1F and 1G are block diagrams depicting embodiments of computing devices useful in connection with the methods and systems described herein.

FIGS. 2A and 2B illustrate solid models of a wearable sensor.

FIG. 2C is a graph illustrating data recorded with a wearable sensor.

FIG. 3 is a block diagram of an embodiment of a wearable sensor for detecting wear compliance.

FIG. 4 is a flow diagram depicting a method for determining the wear state of a wearable sensor.

DETAILED DESCRIPTION

For purposes of reading the description of the various embodiments below, the following descriptions of the sections of the specification and their respective contents may be helpful:

Section A describes a wearable sensor.

Section B describes a network environment and computing environment which may be useful for practicing embodiments described herein.

Section C describes an embodiment of a housing for a wearable sensor.

Section D describes embodiments of systems and methods for detecting wear compliance of a wearable sensor.

A. Wearable Sensor Device

Prior to discussing specific embodiments of the present solution, it may be helpful to describe aspects of the device, operating environment, and associated system components (e.g., hardware elements) in connection with the methods and systems described herein.

Wearable sensor devices may be used to track physical activity, medical conditions, behavioral conditions, or be used as medical alert devices. In a clinical setting, wear compliance (i.e., the wearing of the sensor device by the patient when prescribed) is difficult to assess. Accurately detecting when a wearable sensor is worn by a user enables the correct and accurate measurement of activity and conditions by the sensing device.

FIG. 1A illustrates a block diagram of an embodiment of a wearable sensor 150. As described below, the outward form (i.e., housing) of the wearable sensor 150 can include many shapes, and in general may be reversibly coupled to a wearer. Internally, the wearable sensor 150 includes a processor (also referred to as a CPU) 121, which is powered by a battery 152. Also, the wearable sensor 150 can include a plurality of sensors 153(1)-153(N) (collectively referred to as sensors 153). The wearable sensor 150 can monitor behavioral and/or environmental conditions and store data into the storage device 128. The wearable sensor 150 can communicate with other devices through a data transmitter and receiver 155 module (also referred to as a TX/RX module 155).

The processor 121 and storage device 128 are discussed further in relation to FIGS. 1E and 1F, but in general the processor 121 is a microcontroller or central processing unit capable of executing the instructions and methods described herein. The processor 121 can include one or more analog to digital converters (A/D converters). The A/D converters may be used by the processor 121 to sample and record data from the plurality of sensors 153. In some implementations, the sensor 153 generates a digital signal, which the processor 121 can input without analog to digital conversion. Similarly, the storage device 128 may be any storage device capable of storing the instructions executed by the processor 121. In some implementations, the storage device 128 is a component of the processor 128.

In some embodiments, the storage device 128 is configured to store data obtained via the sensors 153 and/or calculations made by the processor 121. In some implementations, the storage device 128 is a stand-alone component. For example, the storage device 128 can be removeably coupled to the wearable sensor or it may be permanently coupled to the wearable sensor. For example, the storage device 128 may include a Secure Digital (SD) or other removable non-volatile memory card. In some implementations, the storage device 128 is a component of the processor 121. For example, the storage device 128 can be flash memory within the processor 121.

The wearable sensor 150 may store data in the storage device 128 prior to transmitting the data to a second device. For example, the wearable sensor 150 may transmit data to the second device at predetermined intervals (e.g., once every hour or day). In another example, the wearable sensor 150 may store data until the second device is in range, such that the data may be transferred to the second device. For example, the second device may be a base station located within the user's home. When the user (and wearable sensor 150) is away from their home, the wearable sensor 150 may store the data collected during that time onto the storage device 128. When the user arrives home the wearable sensor 150 may detect the base station and transmit the data to the base station. In some implementations, the storage device 128 is a circular storage device (e.g., a circular buffer), such that old data is automatically overwritten once the storage device 128 is full. For example, the storage device 128 may maintain the most recent day, week, or month's worth of data. In some implementations, the data stored in the buffer is divided into a plurality of subsections (e.g., newest, recent, and old data). The wearable sensor 150 may transmit one or more of the subsections to the base station responsive to a policy. For example, the wearable sensor 150 may automatically re-transmit older data stored in the circular buffer when sending new data to ensure that the old data was properly received at the base station.

The wearable sensor 150 also includes TX/RX module 155. As described below, the TX/RX module 155 enables the wearable sensor 150 to communicate with other devices. The other devices can include other wearable sensors 150, base stations, mobile phones, computers, servers, or any combination thereof. In some implementations, the wearable sensor 150 includes a plurality of TX/RX modules 155. For example, the wearable sensor 150 may include a WiFi radio and a Bluetooth radio for wireless communication and also include a universal serial bus (USB) for wired connections to other devices. In some implementations, the TX/RX modules 155 may use IEEE 802.11, Zigbee, USB, Bluetooth, Bluetooth low energy (e.g., v4), TCP/IP, sub-1 GHz (e.g. 315 MHz 433 Mhz 900 MHz) protocols such as SimpliciTI, or any combination thereof to communicate with other devices. One of ordinary skill in the art will recognize that other wired and/or wireless communication protocols may be used in the methods and systems described herein. In some implementations, the TX/RX module 155 is a component of the processor 121.

Power is supplied to the wearable sensor 150 by a battery 152 (or similar power storage device). In some implementations, the battery 152 is coupled to an energy harvester that collects environmental energy (e.g., a solar cell on the face of the wearable sensor 150). The battery 152 can supply between about 1.5 V and 9 V to the processor 121 and/or other components of the wearable sensor 150. In some implementations, the battery 152 lasts about 1-3, 2-5, 4-10, or 7-14 days before needing replacement or recharging. In other implementations, the battery 152 can last 2-6 months, 3-9 months, 8-12 months, or 1-3 years without replacement or recharging. In some implementations, the battery 152 may be a rechargeable battery. In some implementations, the rechargeable battery is charged through the above described USB port that may also be used for wired communication. The battery may be a disposable battery (e.g., a coincell battery). In some implementations, the wearable sensor 150 includes a plurality of batteries 152. The plurality of batteries 152 may be used to extend the time between battery replacement/recharging. In other implementations, one of the plurality of batteries 152 may act as a backup. For example, the backup battery may power the device in the event that the main battery 152 loses power. Similarly, the backup battery may power the wearable sensor 150 during periods of time when the main battery 152 is being replaced.

As illustrated in FIG. 1A, the wearable sensor 150 includes at least one sensor 153. The sensors 153 may be used to monitor and record a plurality of environmental parameters, physical activity parameters, behavioral parameters and medical parameters. For example, the sensors 153 enable the processor 151 to monitor factors such as temperature, light conditions, humidity, skin capacitance, skin resistance, UV light, air quality, ultrasound, FM/AM signal strength, heart rate, pulse oximetry, blood pressure, magnetometer, gas composition, pressure, altitude, breathing rate, force, location, proximity, or any combination thereof. In some implementations, the sensor 153 is a camera. The sensors 153 may measure physical activity parameters such as orientation, acceleration, position (relative or exact), number of steps taken over a period of time, overall activity level, impact (or shock) levels or any combination thereof.

The sensors 153 can include one or more of an accelerometer, gyroscope, passive vibration sensor, light sensor, temperature sensor, altitude sensor, and galvanic response sensor, GPS receiver, auditory sensors (i.e., microphone). The sensors 153 may be internal and/or external to the wearable sensor 150. For example, an accelerometer may be housed within the wearable sensor 150, while a light sensor may be disposed at least partially on the surface of the wearable sensor 150 such that it may detect the ambient lighting conditions. The processor 121 may sample the signal created by the sensors 153 at a plurality of sampling frequencies. For example, the processor 121 may sample the sensors 153 at about 0.25 Hz-5 Hz, 5 Hz-25 Hz, 25 Hz-50 Hz, 50 Hz-100 Hz, or greater than 100 Hz. In embodiments with a plurality of sensors 153, each of the sensors 153 may be sampled at a different frequency.

FIG. 1B illustrates an exemplary schematic of a base station 160 that may be used with the wearable sensor 150. The base station 160 may be a custom built device, general computing device and/or mobile phone. For example, the base station 160 may be any one of the below described client devices 102. As illustrated, the base station 160 may include one or more processors 121 and storage devices 128. The base station 160 also includes a power supply 161 and transmitters and receivers modules 162(1)-162(N) (collectively referred to as TX/RX modules 162). The power supply 161 can power the base station 160 through battery power and/or alternating current power. In some implementations, the base station 160 may include sensors similar to the above described sensors 153 of the wearable sensor 150.

The TX/RX modules 162 may be the similar to the TX/RX modules 155 in the wearable sensor 150. The TX/RX modules 162 may be configured to include a built-in network adapter, network interface card, PCMCIA network card, EXPRESSCARD network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable to communicating with devices and/or networks described herein. In some implementations, the base station 160 may include a first TX/RX module 162 that communicates with one or more wearable sensors 150 and a second TX/RX module 162 that communicates with a network.

FIG. 1C illustrates an environment 180 in which the wearable sensor 150 and base station 160 may be used. The environment 180 includes a structure 181 (e.g., a house), which includes a plurality of walls 182. As illustrated, each room of the structure 181 includes a base station 160. One of ordinary skill in the art will recognize that more than, or less than, four base stations 160 may be used with a base station 160. For example, a base station 160 may be configured to transmit a wireless signal that is detectable throughout substantially an entire house or structure. In other implementations, a wearable sensor 150 may be used without a base station 160.

The base stations 160 are in communication with a network 104. The connection may be wired or wireless. In some implementations, the communication between any two devices (e.g. a wearable sensor 150 and base station 160) in the environment 180 is encrypted. The environment 180 also includes at least one client device 102 and a server 106 are also connected to the network 104. The network 104 is described in greater detail below, but in general, the network 104 can include WANs, LANs, wired telephone networks, wireless telephone networks, or any combination thereof.

As illustrated, the wearable sensor 150 is in wireless communication with each of the base stations 160. In some implementations, the wearable sensor 150 may detect the signal 183 from each of the base stations 160, but only transmit data to a subset of the base stations 160 at any given time. In some implementations, the wearable sensor 150 and/or base stations 160 can determine the relative strength of the wireless connection between the wearable sensor 150 and base station 160. Using the relative strengths of the wireless connections, the relative position of the wearable sensor 150 can be determined. In some implementations, the relative strengths of the wireless connection is determined responsive to a received signal strength indication (RSSI) or a link quality indication (LQI). In some implementations, the relative position of the wearable sensor 150 can be determined using 1, 2, 3, 4, 5, or more base stations 160. For example, a user may have a first base station 160 located in their bedroom and a second base station 160 located in their living room. When the user and wearable sensor 150 are in the user's bedroom, the first base station 160 may have a relatively stronger connection to the wearable sensor 150 when compared to the second base station 160. Responsive to the relative connection strengths between the wearable sensor 150 and first base station 160 and wearable sensor 150 and the second base station 160 it can be determined that the user and wearable sensor 200 are located in the user's bedroom. In some implementations, a distribution of the RSSI and LQI within the environment 180 can be modeled. Applying, the wearable sensor's current wireless connection strength, the location of the wearable sensor 150 can be predicted using, for example, a particle filter.

The plurality of devices within the environment 180 are connected through the network 104. In some implementations, the wearable sensor 150 is connected to the network 104 through one or more base stations 160 via a wireless connection 183. In other implementations, the wearable sensor 150 is connected directly to the network 104 through connection 184. In yet other implementations, the wearable sensor 150 docks (or establishes a wired connection) with a base station 160 to connect to the network 104. For example, the base station 160 may collect data throughout the day. After data has been collected the user may transmit the collected data to the server 106 by connecting the wearable sensor 150 to a base station 160.

The wearable sensor 150 may communicate with other devices in the environment 180 to transmit data collected via the sensors 153. The wearable sensor 150 may establish a connection with one or more of the devices in the environment 180 at predetermined intervals (e.g., hourly, daily, weekly, or monthly), when the amount of data stored on its storage device 128 reaches a predetermined threshold, and/or the wearable sensor 150 may be in constant communication with the one or more devices when they are in range to enable wireless communication.

The environment 180 also includes a client device 102. Each of the client device 102, network 104, and server 106 are described in greater detail in relation to FIGS. 1E-1G. In brief, the client device 102 may include smart phones or other mobile devices. In some implementations, the client device 102 can also act as a base station 160. For example, the wearable sensor 150 may connect to the client device 102 via a Bluetooth connection to deliver data to the client device 102. The client device 102 may then establish a connection with the server 106 to deliver the data to the server 106. In some implementations, the client device 102 is in the possession of the user of the wearable sensor 150 and, in other implementations, the client device 102 is in the possession of a person other than the user of the wearable sensor 150. The environment 180 may include a plurality of client devices 102 that each correspond to a single wearable sensor 150. For example, a first client device 102 may be owned by the user of the wearable sensor 150 and a second client device 102 may be owned by a care taker of the user. Notifications or data corresponding to the wearable sensor 150 may be viewed by both the user and the care taker via their respective client devices 102.

In some implementations, the client device 102 may be used as an interface to the wearable sensor 150. The client device 102 may execute an application that enables a user to interact with the wearable sensor 150. The interaction may include turning the wearable sensor 150 on or off, marking events, viewing data, enabling features, or any combination thereof. In some implementations, a user can use the application to share and view data from (or with) loved-ones. Caretakers or medical professionals may use the application to view data from a patient. The application may also be used to transmit medical alters responsive to their detection by the wearable sensor 150. For example, the wearable sensor 150 may obtain motion data via a sensor 153 and transmit the data to the server 106. The user may view the trend of the motion data via the user's client device 102. In some implementations, alerts, updates, or data from an associated wearable sensor 150 is pushed to the client device 102. For example, the wearable sensor 150 may be configured to detect if the wearable sensor 150 is not being worn. If the wearable sensor 150 determines that it is not being worn, an alert may be sent to the client device 102 of a caretaker.

One of ordinary skill in the art will recognize that the methods described herein may occur on the wearable sensor 150, base station 160, client device 102, server 106, or any combination thereof. For example, the wearable sensor 150 may record motion data and transmit the data to the server 106. The server 106 may process the data to determine the wear state of the wearable sensor 150.

B. Computing and Network Environment

Prior to discussing specific embodiments of the present solution, it may be helpful to describe aspects of the operating environment as well as associated system components (e.g., hardware elements) in connection with the methods and systems described herein. Referring to FIG. 1E, an embodiment of a network environment is depicted. In brief overview, the network environment includes one or more clients 102 a-102 n (also generally referred to as local machine(s) 102, client(s) 102, client node(s) 102, client machine(s) 102, client computer(s) 102, client device(s) 102, endpoint(s) 102, or endpoint node(s) 102) in communication with one or more servers 106 a-106 n (also generally referred to as server(s) 106, node 106, or remote machine(s) 106) via one or more networks 104. In some embodiments, a client 102 has the capacity to function as both a client node seeking access to resources provided by a server and as a server providing access to hosted resources for other clients 102 a-102 n.

Although FIG. 1E shows a network 104 between the clients 102 and the servers 106, the clients 102 and the servers 106 may be on the same network 104. In some embodiments, there are multiple networks 104 between the clients 102 and the servers 106. In one of these embodiments, a network 104′ (not shown) may be a private network and a network 104 may be a public network. In another of these embodiments, a network 104 may be a private network and a network 104′ a public network. In still another of these embodiments, networks 104 and 104′ may both be private networks.

The network 104 may be connected via wired or wireless links. Wired links may include Digital Subscriber Line (DSL), coaxial cable lines, or optical fiber lines. The wireless links may include BLUETOOTH, Wi-Fi, public switching telephone network, Worldwide Interoperability for Microwave Access (WiMAX), an infrared channel or satellite band. The wireless links may also include any cellular network standards used to communicate among mobile devices, including standards that qualify as 1G, 2G, 3G, or 4G. The network standards may qualify as one or more generation of mobile telecommunication standards by fulfilling a specification or standards such as the specifications maintained by International Telecommunication Union. The 3G standards, for example, may correspond to the International Mobile Telecommunications-2000 (IMT-2000) specification, and the 4G standards may correspond to the International Mobile Telecommunications Advanced (IMT-Advanced) specification. Examples of cellular network standards include AMPS, GSM, GPRS, UMTS, LTE, LTE Advanced, Mobile WiMAX, and WiMAX-Advanced. Cellular network standards may use various channel access methods e.g. FDMA, TDMA, CDMA, or SDMA. In some embodiments, different types of data may be transmitted via different links and standards. In other embodiments, the same types of data may be transmitted via different links and standards.

The network 104 may be any type and/or form of network. The geographical scope of the network 104 may vary widely and the network 104 can be a body area network (BAN), a personal area network (PAN), a local-area network (LAN), e.g. Intranet, a metropolitan area network (MAN), a wide area network (WAN), or the Internet. The topology of the network 104 may be of any form and may include, e.g., any of the following: point-to-point, bus, star, ring, mesh, or tree. The network 104 may be an overlay network which is virtual and sits on top of one or more layers of other networks 104′. The network 104 may be of any such network topology as known to those ordinarily skilled in the art capable of supporting the operations described herein. The network 104 may utilize different techniques and layers or stacks of protocols, including, e.g., the Ethernet protocol, the internet protocol suite (TCP/IP), the ATM (Asynchronous Transfer Mode) technique, the SONET (Synchronous Optical Networking) protocol, or the SDH (Synchronous Digital Hierarchy) protocol. The TCP/IP internet protocol suite may include application layer, transport layer, internet layer (including, e.g., IPv6), or the link layer. The network 104 may be a type of a broadcast network, a telecommunications network, a data communication network, or a computer network.

In some embodiments, the system may include multiple, logically-grouped servers 106. In one of these embodiments, the logical group of servers may be referred to as a server farm 38 or a machine farm 38. In another of these embodiments, the servers 106 may be geographically dispersed. In other embodiments, a machine farm 38 may be administered as a single entity. In still other embodiments, the machine farm 38 includes a plurality of machine farms 38. The servers 106 within each machine farm 38 can be heterogeneous—one or more of the servers 106 or machines 106 can operate according to one type of operating system platform (e.g., WINDOWS NT, manufactured by Microsoft Corp. of Redmond, Wash.), while one or more of the other servers 106 can operate on according to another type of operating system platform (e.g., Unix, Linux, or Mac OS X).

In one embodiment, servers 106 in the machine farm 38 may be stored in high-density rack systems, along with associated storage systems, and located in an enterprise data center. In this embodiment, consolidating the servers 106 in this way may improve system manageability, data security, the physical security of the system, and system performance by locating servers 106 and high performance storage systems on localized high performance networks. Centralizing the servers 106 and storage systems and coupling them with advanced system management tools allows more efficient use of server resources.

The servers 106 of each machine farm 38 do not need to be physically proximate to another server 106 in the same machine farm 38. Thus, the group of servers 106 logically grouped as a machine farm 38 may be interconnected using a wide-area network (WAN) connection or a metropolitan-area network (MAN) connection. For example, a machine farm 38 may include servers 106 physically located in different continents or different regions of a continent, country, state, city, campus, or room. Data transmission speeds between servers 106 in the machine farm 38 can be increased if the servers 106 are connected using a local-area network (LAN) connection or some form of direct connection. Additionally, a heterogeneous machine farm 38 may include one or more servers 106 operating according to a type of operating system, while one or more other servers 106 execute one or more types of hypervisors rather than operating systems. In these embodiments, hypervisors may be used to emulate virtual hardware, partition physical hardware, virtualize physical hardware, and execute virtual machines that provide access to computing environments, allowing multiple operating systems to run concurrently on a host computer. Native hypervisors may run directly on the host computer. Hypervisors may include VMware ESX/ESXi, manufactured by VMWare, Inc., of Palo Alto, Calif.; the Xen hypervisor, an open source product whose development is overseen by Citrix Systems, Inc.; the HYPER-V hypervisors provided by Microsoft or others. Hosted hypervisors may run within an operating system on a second software level. Examples of hosted hypervisors may include VMware Workstation and VIRTUALBOX.

Management of the machine farm 38 may be de-centralized. For example, one or more servers 106 may comprise components, subsystems and modules to support one or more management services for the machine farm 38. In one of these embodiments, one or more servers 106 provide functionality for management of dynamic data, including techniques for handling failover, data replication, and increasing the robustness of the machine farm 38. Each server 106 may communicate with a persistent store and, in some embodiments, with a dynamic store. In some embodiments, one or more servers 106 may process a distributed data set. The distributed data set may be distributed over one or more servers 106 or storage systems. In these embodiments, the servers 106 may use MapReduce or Hadoop to process the data.

Server 106 may be a file server, application server, web server, proxy server, appliance, network appliance, gateway, gateway server, virtualization server, deployment server, SSL VPN server, or firewall. In one embodiment, the server 106 may be referred to as a remote machine or a node. In another embodiment, a plurality of nodes 290 may be in the path between any two communicating servers.

Referring to FIG. 1F, a cloud computing environment is depicted. A cloud computing environment may provide client 102 with one or more resources provided by a network environment. The cloud computing environment may include one or more clients 102 a-102 n, in communication with the cloud 108 over one or more networks 104. Clients 102 may include, e.g., thick clients, thin clients, and zero clients. A thick client may provide at least some functionality even when disconnected from the cloud 108 or servers 106. A thin client or a zero client may depend on the connection to the cloud 108 or server 106 to provide functionality. A zero client may depend on the cloud 108 or other networks 104 or servers 106 to retrieve operating system data for the client device. The cloud 108 may include back end platforms, e.g., servers 106, storage, server farms or data centers.

The cloud 108 may be public, private, or hybrid. Public clouds may include public servers 106 that are maintained by third parties to the clients 102 or the owners of the clients. The servers 106 may be located off-site in remote geographical locations as disclosed above or otherwise. Public clouds may be connected to the servers 106 over a public network. Private clouds may include private servers 106 that are physically maintained by clients 102 or owners of clients. Private clouds may be connected to the servers 106 over a private network 104. Hybrid clouds 108 may include both the private and public networks 104 and servers 106.

The cloud 108 may also include a cloud based delivery, e.g. Software as a Service (SaaS) 110, Platform as a Service (PaaS) 112, and Infrastructure as a Service (IaaS) 114. IaaS may refer to a user renting the use of infrastructure resources that are needed during a specified time period. IaaS providers may offer storage, networking, servers or virtualization resources from large pools, allowing the users to quickly scale up by accessing more resources as needed. Examples of IaaS include AMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Wash., RACKSPACE CLOUD provided by Rackspace US, Inc., of San Antonio, Tex., Google Compute Engine provided by Google Inc. of Mountain View, Calif., or RIGHTSCALE provided by RightScale, Inc., of Santa Barbara, Calif. PaaS providers may offer functionality provided by IaaS, including, e.g., storage, networking, servers or virtualization, as well as additional resources such as, e.g., the operating system, middleware, or runtime resources. Examples of PaaS include WINDOWS AZURE provided by Microsoft Corporation of Redmond, Wash., Google App Engine provided by Google Inc., and HEROKU provided by Heroku, Inc. of San Francisco, Calif. SaaS providers may offer the resources that PaaS provides, including storage, networking, servers, virtualization, operating system, middleware, or runtime resources. In some embodiments, SaaS providers may offer additional resources including, e.g., data and application resources. Examples of SaaS include GOOGLE APPS provided by Google Inc., SALESFORCE provided by Salesforce.com Inc. of San Francisco, Calif., or OFFICE 365 provided by Microsoft Corporation. Examples of SaaS may also include data storage providers, e.g. DROPBOX provided by Dropbox, Inc. of San Francisco, Calif., Microsoft SKYDRIVE provided by Microsoft Corporation, Google Drive provided by Google Inc., or Apple ICLOUD provided by Apple Inc. of Cupertino, Calif.

Clients 102 may access IaaS resources with one or more IaaS standards, including, e.g., Amazon Elastic Compute Cloud (EC2), Open Cloud Computing Interface (OCCI), Cloud Infrastructure Management Interface (CIMI), or OpenStack standards. Some IaaS standards may allow clients access to resources over HTTP, and may use Representational State Transfer (REST) protocol or Simple Object Access Protocol (SOAP). Clients 102 may access PaaS resources with different PaaS interfaces. Some PaaS interfaces use HTTP packages, standard Java APIs, JavaMail API, Java Data Objects (JDO), Java Persistence API (JPA), Python APIs, web integration APIs for different programming languages including, e.g., Rack for Ruby, WSGI for Python, or PSGI for Perl, or other APIs that may be built on REST, HTTP, XML, or other protocols. Clients 102 may access SaaS resources through the use of web-based user interfaces, provided by a web browser (e.g. GOOGLE CHROME, Microsoft INTERNET EXPLORER, or Mozilla Firefox provided by Mozilla Foundation of Mountain View, Calif.). Clients 102 may also access SaaS resources through smartphone or tablet applications, including, e.g., Salesforce Sales Cloud, or Google Drive app. Clients 102 may also access SaaS resources through the client operating system, including, e.g., Windows file system for DROPBOX.

In some embodiments, access to IaaS, PaaS, or SaaS resources may be authenticated. For example, a server or authentication server may authenticate a user via security certificates, HTTPS, or API keys. API keys may include various encryption standards such as, e.g., Advanced Encryption Standard (AES). Data resources may be sent over Transport Layer Security (TLS) or Secure Sockets Layer (SSL).

The client 102 and server 106 may be deployed as and/or executed on any type and form of computing device, e.g. a computer, network device or appliance capable of communicating on any type and form of network and performing the operations described herein. FIGS. 1G and 1D depict block diagrams of a computing device 100 useful for practicing an embodiment of the client 102 or a server 106. As shown in FIGS. 1G and 1D, each computing device 100 includes a central processing unit 121, and a main memory unit 122. As shown in FIG. 1G, a computing device 100 may include a storage device 128, an installation device 116, a network interface 118, an I/O controller 123, display devices 124 a-124 n, a keyboard 126 and a pointing device 127, e.g. a mouse. The storage device 128 may include, without limitation, an operating system, software, and a software of a wear detection module 120. As shown in FIG. 1D, each computing device 100 may also include additional optional elements, e.g. a memory port 103, a bridge 170, one or more input/output devices 130 a-130 n (generally referred to using reference numeral 130), and a cache memory 140 in communication with the central processing unit 121.

The central processing unit 121 is any logic circuitry that responds to and processes instructions fetched from the main memory unit 122. In many embodiments, the central processing unit 121 is provided by a microprocessor unit, e.g.: those manufactured by Intel Corporation of Mountain View, Calif.; those manufactured by Motorola Corporation of Schaumburg, Ill.; the ARM processor and TEGRA system on a chip (SoC) manufactured by Nvidia of Santa Clara, Calif.; the POWER7 processor, those manufactured by International Business Machines of White Plains, N.Y.; or those manufactured by Advanced Micro Devices of Sunnyvale, Calif. The computing device 100 may be based on any of these processors, or any other processor capable of operating as described herein. The central processing unit 121 may utilize instruction level parallelism, thread level parallelism, different levels of cache, and multi-core processors. A multi-core processor may include two or more processing units on a single computing component. Examples of a multi-core processors include the AMD PHENOM IIX2, INTEL CORE i5 and INTEL CORE i7.

Main memory unit 122 may include one or more memory chips capable of storing data and allowing any storage location to be directly accessed by the microprocessor 121. Main memory unit 122 may be volatile and faster than storage 128 memory. Main memory units 122 may be Dynamic random access memory (DRAM) or any variants, including static random access memory (SRAM), Burst SRAM or SynchBurst SRAM (BSRAM), Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended Data Output RAM (EDO RAM), Extended Data Output DRAM (EDO DRAM), Burst Extended Data Output DRAM (REDO DRAM), Single Data Rate Synchronous DRAM (SDR SDRAM), Double Data Rate SDRAM (DDR SDRAM), Direct Rambus DRAM (DRDRAM), or Extreme Data Rate DRAM (XDR DRAM). In some embodiments, the main memory 122 or the storage 128 may be non-volatile; e.g., non-volatile read access memory (NVRAM), flash memory non-volatile static RAM (nvSRAM), Ferroelectric RAM (FeRAM), Magnetoresistive RAM (MRAM), Phase-change memory (PRAM), conductive-bridging RAM (CBRAM), Silicon-Oxide-Nitride-Oxide-Silicon (SONOS), Resistive RAM (RRAM), Racetrack, Nano-RAM (NRAM), or Millipede memory. The main memory 122 may be based on any of the above described memory chips, or any other available memory chips capable of operating as described herein. In the embodiment shown in FIG. 1G, the processor 121 communicates with main memory 122 via a system bus 150 (described in more detail below). FIG. 1D depicts an embodiment of a computing device 100 in which the processor communicates directly with main memory 122 via a memory port 103. For example, in FIG. 1D the main memory 122 may be DRDRAM.

FIG. 1D depicts an embodiment in which the main processor 121 communicates directly with cache memory 140 via a secondary bus, sometimes referred to as a backside bus. In other embodiments, the main processor 121 communicates with cache memory 140 using the system bus 150. Cache memory 140 typically has a faster response time than main memory 122 and is typically provided by SRAM, BSRAM, or EDRAM. In the embodiment shown in FIG. 1D, the processor 121 communicates with various I/O devices 130 via a local system bus 150. Various buses may be used to connect the central processing unit 121 to any of the I/O devices 130, including a PCI bus, a PCI-X bus, or a PCI-Express bus, or a NuBus. For embodiments in which the I/O device is a video display 124, the processor 121 may use an Advanced Graphics Port (AGP) to communicate with the display 124 or the I/O controller 123 for the display 124. FIG. 1D depicts an embodiment of a computer 100 in which the main processor 121 communicates directly with I/O device 130 b or other processors 121′ via HYPERTRANSPORT, RAPIDIO, or INFINIBAND communications technology. FIG. 1D also depicts an embodiment in which local busses and direct communication are mixed: the processor 121 communicates with I/O device 130 a using a local interconnect bus while communicating with I/O device 130 b directly.

A wide variety of I/O devices 130 a-130 n may be present in the computing device 100. Input devices may include keyboards, mice, trackpads, trackballs, touchpads, touch mice, multi-touch touchpads and touch mice, microphones, multi-array microphones, drawing tablets, cameras, single-lens reflex camera (SLR), digital SLR (DSLR), CMOS sensors, accelerometers, infrared optical sensors, pressure sensors, magnetometer sensors, angular rate sensors, depth sensors, proximity sensors, ambient light sensors, gyroscopic sensors, or other sensors. Output devices may include video displays, graphical displays, speakers, headphones, inkjet printers, laser printers, and 3D printers.

Devices 130 a-130 n may include a combination of multiple input or output devices, including, e.g., Microsoft KINECT, Nintendo Wiimote for the WII, Nintendo WII U GAMEPAD, or Apple IPHONE. Some devices 130 a-130 n allow gesture recognition inputs through combining some of the inputs and outputs. Some devices 130 a-130 n provides for facial recognition which may be utilized as an input for different purposes including authentication and other commands. Some devices 130 a-130 n provides for voice recognition and inputs, including, e.g., Microsoft KINECT, SIRI for IPHONE by Apple, Google Now or Google Voice Search.

Additional devices 130 a-130 n have both input and output capabilities, including, e.g., haptic feedback devices, touchscreen displays, or multi-touch displays. Touchscreen, multi-touch displays, touchpads, touch mice, or other touch sensing devices may use different technologies to sense touch, including, e.g., capacitive, surface capacitive, projected capacitive touch (PCT), in-cell capacitive, resistive, infrared, waveguide, dispersive signal touch (DST), in-cell optical, surface acoustic wave (SAW), bending wave touch (BWT), or force-based sensing technologies. Some multi-touch devices may allow two or more contact points with the surface, allowing advanced functionality including, e.g., pinch, spread, rotate, scroll, or other gestures. Some touchscreen devices, including, e.g., Microsoft PIXELSENSE or Multi-Touch Collaboration Wall, may have larger surfaces, such as on a table-top or on a wall, and may also interact with other electronic devices. Some I/O devices 130 a-130 n, display devices 124 a-124 n or group of devices may be augment reality devices. The I/O devices may be controlled by an I/O controller 123 as shown in FIG. 1G. The I/O controller may control one or more I/O devices, such as, e.g., a keyboard 126 and a pointing device 127, e.g., a mouse or optical pen. Furthermore, an I/O device may also provide storage and/or an installation medium 116 for the computing device 100. In still other embodiments, the computing device 100 may provide USB connections (not shown) to receive handheld USB storage devices. In further embodiments, an I/O device 130 may be a bridge between the system bus 150 and an external communication bus, e.g. a USB bus, a SCSI bus, a FireWire bus, an Ethernet bus, a Gigabit Ethernet bus, a Fibre Channel bus, or a Thunderbolt bus.

In some embodiments, display devices 124 a-124 n may be connected to I/O controller 123. Display devices may include, e.g., liquid crystal displays (LCD), thin film transistor LCD (TFT-LCD), blue phase LCD, electronic papers (e-ink) displays, flexile displays, light emitting diode displays (LED), digital light processing (DLP) displays, liquid crystal on silicon (LCOS) displays, organic light-emitting diode (OLED) displays, active-matrix organic light-emitting diode (AMOLED) displays, liquid crystal laser displays, time-multiplexed optical shutter (TMOS) displays, or 3D displays. Examples of 3D displays may use, e.g. stereoscopy, polarization filters, active shutters, or autostereoscopy. Display devices 124 a-124 n may also be a head-mounted display (HMD). In some embodiments, display devices 124 a-124 n or the corresponding I/O controllers 123 may be controlled through or have hardware support for OPENGL or DIRECTX API or other graphics libraries.

In some embodiments, the computing device 100 may include or connect to multiple display devices 124 a-124 n, which each may be of the same or different type and/or form. As such, any of the I/O devices 130 a-130 n and/or the I/O controller 123 may include any type and/or form of suitable hardware, software, or combination of hardware and software to support, enable or provide for the connection and use of multiple display devices 124 a-124 n by the computing device 100. For example, the computing device 100 may include any type and/or form of video adapter, video card, driver, and/or library to interface, communicate, connect or otherwise use the display devices 124 a-124 n. In one embodiment, a video adapter may include multiple connectors to interface to multiple display devices 124 a-124 n. In other embodiments, the computing device 100 may include multiple video adapters, with each video adapter connected to one or more of the display devices 124 a-124 n. In some embodiments, any portion of the operating system of the computing device 100 may be configured for using multiple displays 124 a-124 n. In other embodiments, one or more of the display devices 124 a-124 n may be provided by one or more other computing devices 100 a or 100 b connected to the computing device 100, via the network 104. In some embodiments software may be designed and constructed to use another computer's display device as a second display device 124 a for the computing device 100. For example, in one embodiment, an Apple iPad may connect to a computing device 100 and use the display of the device 100 as an additional display screen that may be used as an extended desktop. One ordinarily skilled in the art will recognize and appreciate the various ways and embodiments that a computing device 100 may be configured to have multiple display devices 124 a-124 n.

Referring again to FIG. 1G, the computing device 100 may comprise a storage device 128 (e.g. one or more hard disk drives or redundant arrays of independent disks) for storing an operating system or other related software, and for storing application software programs such as any program related to the software 120 for the experiment tracker system. Examples of storage device 128 include, e.g., hard disk drive (HDD); optical drive including CD drive, DVD drive, or BLU-RAY drive; solid-state drive (SSD); USB flash drive; or any other device suitable for storing data. Some storage devices may include multiple volatile and non-volatile memories, including, e.g., solid state hybrid drives that combine hard disks with solid state cache. Some storage device 128 may be non-volatile, mutable, or read-only. Some storage device 128 may be internal and connect to the computing device 100 via a bus 150. Some storage device 128 may be external and connect to the computing device 100 via a I/O device 130 that provides an external bus. Some storage device 128 may connect to the computing device 100 via the network interface 118 over a network 104, including, e.g., the Remote Disk for MACBOOK AIR by Apple. Some client devices 100 may not require a non-volatile storage device 128 and may be thin clients or zero clients 102. Some storage device 128 may also be used as a installation device 116, and may be suitable for installing software and programs. Additionally, the operating system and the software can be run from a bootable medium, for example, a bootable CD, e.g. KNOPPIX, a bootable CD for GNU/Linux that is available as a GNU/Linux distribution from knoppix.net.

Client device 100 may also install software or application from an application distribution platform. Examples of application distribution platforms include the App Store for iOS provided by Apple, Inc., the Mac App Store provided by Apple, Inc., GOOGLE PLAY for Android OS provided by Google Inc., Chrome Webstore for CHROME OS provided by Google Inc., and Amazon Appstore for Android OS and KINDLE FIRE provided by Amazon.com, Inc. An application distribution platform may facilitate installation of software on a client device 102. An application distribution platform may include a repository of applications on a server 106 or a cloud 108, which the clients 102 a-102 n may access over a network 104. An application distribution platform may include application developed and provided by various developers. A user of a client device 102 may select, purchase and/or download an application via the application distribution platform.

Furthermore, the computing device 100 may include a network interface 118 to interface to the network 104 through a variety of connections including, but not limited to, standard telephone lines LAN or WAN links (e.g., 802.11, T1, T3, Gigabit Ethernet, Infiniband), broadband connections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet, Ethernet-over-SONET, ADSL, VDSL, BPON, GPON, fiber optical including FiOS), wireless connections, or some combination of any or all of the above. Connections can be established using a variety of communication protocols (e.g., TCP/IP, Ethernet, ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), IEEE 802.11E/b/g/n/ac CDMA, GSM, WiMax and direct asynchronous connections). In one embodiment, the computing device 100 communicates with other computing devices 100′ via any type and/or form of gateway or tunneling protocol e.g. Secure Socket Layer (SSL) or Transport Layer Security (TLS), or the Citrix Gateway Protocol manufactured by Citrix Systems, Inc. of Ft. Lauderdale, Fla. The network interface 118 may comprise a built-in network adapter, network interface card, PCMCIA network card, EXPRESSCARD network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the computing device 100 to any type of network capable of communication and performing the operations described herein.

A computing device 100 of the sort depicted in FIGS. 1F and 1G may operate under the control of an operating system, which controls scheduling of tasks and access to system resources. The computing device 100 can be running any operating system such as any of the versions of the MICROSOFT WINDOWS operating systems, the different releases of the Unix and Linux operating systems, any version of the MAC OS for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, any operating systems for mobile computing devices, or any other operating system capable of running on the computing device and performing the operations described herein. Typical operating systems include, but are not limited to: WINDOWS 2000, WINDOWS Server 2012, WINDOWS CE, WINDOWS Phone, WINDOWS XP, WINDOWS VISTA, and WINDOWS 7, WINDOWS RT, and WINDOWS 8 all of which are manufactured by Microsoft Corporation of Redmond, Wash.; MAC OS and iOS, manufactured by Apple, Inc. of Cupertino, Calif.; and Linux, a freely-available operating system, e.g. Linux Mint distribution (“distro”) or Ubuntu, distributed by Canonical Ltd. of London, United Kingdom; or Unix or other Unix-like derivative operating systems; and Android, designed by Google, of Mountain View, Calif., among others. Some operating systems, including, e.g., the CHROME OS by Google, may be used on zero clients or thin clients, including, e.g., CHROMEBOOKS.

The computer system 100 can be any workstation, telephone, desktop computer, laptop or notebook computer, netbook, ULTRABOOK, tablet, server, handheld computer, mobile telephone, smartphone or other portable telecommunications device, media playing device, a gaming system, mobile computing device, or any other type and/or form of computing, telecommunications or media device that is capable of communication. The computer system 100 has sufficient processor power and memory capacity to perform the operations described herein. In some embodiments, the computing device 100 may have different processors, operating systems, and input devices consistent with the device. The Samsung GALAXY smartphones, e.g., operate under the control of Android operating system developed by Google, Inc. GALAXY smartphones receive input via a touch interface.

In some embodiments, the computing device 100 is a gaming system. For example, the computer system 100 may comprise a PLAYSTATION 3, or PERSONAL PLAYSTATION PORTABLE (PSP), or a PLAYSTATION VITA device manufactured by the Sony Corporation of Tokyo, Japan, a NINTENDO DS, NINTENDO 3DS, NINTENDO WII, or a NINTENDO WII U device manufactured by Nintendo Co., Ltd., of Kyoto, Japan, an XBOX 360 device manufactured by the Microsoft Corporation of Redmond, Wash.

In some embodiments, the computing device 100 is a digital audio player such as the Apple IPOD, IPOD Touch, and IPOD NANO lines of devices, manufactured by Apple Computer of Cupertino, Calif. Some digital audio players may have other functionality, including, e.g., a gaming system or any functionality made available by an application from a digital application distribution platform. For example, the IPOD Touch may access the Apple App Store. In some embodiments, the computing device 100 is a portable media player or digital audio player supporting file formats including, but not limited to, MP3, WAV, M4A/AAC, WMA Protected AAC, AIFF, Audible audiobook, Apple Lossless audio file formats and .mov, .m4v, and .mp4MPEG-4 (H.264/MPEG-4 AVC) video file formats.

In some embodiments, the computing device 100 is a tablet e.g. the IPAD line of devices by Apple; GALAXY TAB family of devices by Samsung; or KINDLE FIRE, by Amazon.com, Inc. of Seattle, Wash. In other embodiments, the computing device 100 is a eBook reader, e.g. the KINDLE family of devices by Amazon.com, or NOOK family of devices by Barnes & Noble, Inc. of New York City, N.Y.

In some embodiments, the communications device 102 includes a combination of devices, e.g. a smartphone combined with a digital audio player or portable media player. For example, one of these embodiments is a smartphone, e.g. the IPHONE family of smartphones manufactured by Apple, Inc.; a Samsung GALAXY family of smartphones manufactured by Samsung, Inc; or a Motorola DROID family of smartphones. In yet another embodiment, the communications device 102 is a laptop or desktop computer equipped with a web browser and a microphone and speaker system, e.g. a telephony headset. In these embodiments, the communications devices 102 are web-enabled and can receive and initiate phone calls. In some embodiments, a laptop or desktop computer is also equipped with a webcam or other video capture device that enables video chat and video call.

In some embodiments, the status of one or more machines 102, 106 in the network 104 is monitored, generally as part of network management. In one of these embodiments, the status of a machine may include an identification of load information (e.g., the number of processes on the machine, CPU and memory utilization), of port information (e.g., the number of available communication ports and the port addresses), or of session status (e.g., the duration and type of processes, and whether a process is active or idle). In another of these embodiments, this information may be identified by a plurality of metrics, and the plurality of metrics can be applied at least in part towards decisions in load distribution, network traffic management, and network failure recovery as well as any aspects of operations of the present solution described herein. Aspects of the operating environments and components described above will become apparent in the context of the systems and methods disclosed herein.

C. Wearable Sensor Housing

FIGS. 2A and 2B illustrate isometric and top views, respectively, of an exemplary wearable sensor 200. The wearable sensor 200 includes a housing 201, which is coupled to a user with a strap 202. In some implementations, a user may wear the wearable sensor 200 like a wrist watch. In other implementations, the wearable sensor 200 may include a clip, pin, or similar means to couple the wearable sensor 200 to a user. In some implementations, the wearable sensor 200 is incorporated into a watch or piece of jewelry (e.g., a bracelet or necklace).

The housing 201 houses the components described above in relation to FIG. 1A. The above described sensors 153 may be internal and/or external to the housing 201. For example, the wearable sensor 200 may include an accelerometer, which need not be exposed to the outside environment. In another example, the wearable sensor 200 may include an ambient light sensor that is exposed to the outside environment such that it can measure the light levels. Similarly, the wearable sensor 200 may include a galvanic response sensor on the bottom surface of the housing 201. The galvanic sensor may make contact with a user's skin and be configured to determine when it is in contact with a user's skin and when it is not in contact with a user's skin.

As illustrated, the wearable sensor 200 includes a button 203. In some implementations, the wearable sensor 200 includes a plurality of buttons and/or a screen. In some implementations, the screen is a touch screen. A user may use the bottom 203 to mark events. For example, a user may mark the beginning of physical activity. In some implementations, the button 203 is coupled to an input on the above described processor 121. When the button 203 is depressed, the processor 121 may mark the event. For example, the processor 121 may store a time stamp of when the button 123 was depressed in the storage device 128.

In some implementations, the wearable sensor 200 includes medical alert functionality. The button 203 may be used to request help or to send an alert that a medical incident is occurring. For example, if the user falls the user may press the button 203 and the wearable sensor 200 may send an alert to the client device 102 of a care taker.

In some implementations, the functionality of a button 203 and/or screen located on the wearable sensor 200 is achieved through a client device 102. For example, a user's smart phone may run an application that corresponds to the wearable sensor 200. The application may be in communication with the wearable sensor 200 through a data connection on the phone and wearable sensor 200. The application may provide the user with information relevant to the wearable sensor 200. In some implementations, the application may enable the user to activate features on the wearable sensor 200 as if the user was depressing a physical button on the wearable sensor 200. For example, the wearable sensor 200 may include an “activity button,” that when activated causes the wearable sensor 200 to mark a time stamp indicating when the activity button was depressed in the application.

The wearable sensor may be designed and constructed to have a predetermined shape or form that provides a predetermined orientation when the wearable sensor is lying at rest, falls and lies at rest or is placed down by the wearer on a surface. In some implementations, the wearable sensor 200 is configured such that when not worn, it defaults to a particular orientation. For example, the top of the housing 201 may be dome shaped and weighted to one side. When not worn the wearable sensor 200 may lean to the weighted side (also referred to as the wearable sensor's default orientation). When the wearable sensor 200 (or other device in the environment 180) detects the default orientation, the wearable sensor 200 may determine that it is not being worn.

FIG. 2C depicts a graph of data that may be obtained with the wearable sensor 200. As described above, in some implementations, the wearable sensor 200 detects orientation. The solid line 250 indicates the wearable sensor's orientation along a first axis and the dotted line 252 indicates the wearable sensor's orientation along a second axis. The method of determining if movement has occurred based on orientation is described in greater detail below. In general, the change in orientation is correlated with a movement of the wearable sensor 200. For example, in the time epochs 254 and 256 relatively large fluctuations are seen in both the orientation signals 250 and 252. In some implementations, the wearable sensor 200 (or other device described herein) quantifies movement to determine if the wearable sensor 200 is being worn during different epochs.

In some implementations, basing the detection of movement on orientation enables sampling at a relatively lower sampling rate when compared to basing the detection of movement on acceleration. The line 260 indicates an acceleration signal of the same wearable sensor 200. As illustrated, the acceleration signal is relatively steady except for during epochs of large movement. However, to capture the rapid change in accelerations, the sampling frequency must be high enough to accurately recreate the signal. For example, if a sample is only recoded at t₀, t₁, and t₂, (far below the Nyquist frequency of the acceleration signal 260) then the acceleration signal would appear flat throughout the recording and it would appear as if no movement occurred. However, sampling at t₀, t₁, and t₂, provides data points with substantially different orientation values, and thus movement information may be determined from low sampled orientation data.

D. Detecting Wear and Non-Wear State for a Wearable Sensor

The systems and methods described herein are related to detecting if a wearable sensor is being or was worn during a specific time epoch. More particularly, the system and methods enable the detection of a wear state that is both accurate and power efficient. As described above, in some implementations, the wearable sensor can be a medical alert device, panic button device, or physical activity monitor. The wearable sensor enables the passive measurement of behaviors in ambulatory environments. The wearable sensor may sync or communicate with a plurality of devices such as other wearable sensors, smart phones, computers, or any combination thereof. In some implementations, the wearable sensor 200 is used by caretakers to remotely monitor patients.

As used herein, the term state is used to describe the wearable sensor's relation to a user. For example, during a non-wear state the user is not wearing the wearable sensor and during a wear state the wearable sensor is being worn by the user. In some implementations, the state can also correspond to an activity. For example, the state may indicate that the user is wearing the wearable sensor and also sleeping or wearing the wearable sensor and involved in a physical activity.

While the invention has been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention described in this disclosure. For example, the methods described herein may be included in as a module that is added to an already available wearable sensor or custom built wearable sensor such as the wearable sensor described herein.

FIG. 3 depicts a schematic of an exemplary wearable sensor 300. The wearable sensor 300 is configured for the power-efficient and accurate determination of wear state. The wearable sensor 300 includes one or more transmitters and receiver modules 355 (also referred to as TX/RX modules 355), a storage device 328, a plurality of sensors 353(1)-353(N), and a processor 321. Each of these components are similar to the components described above in relation to FIGS. 1A-1G.

The wearable sensor 300 also includes a wear detection module 310 and a movement detection module 320. The wear detection module 310 and the movement detection module 320 are responsible for the detection of wear state and movement, respectively. In some implementations, the wear detection module 310 and the movements detection module 320 include processor executable instructions that are stored in the storage device 328. The storage device 328 also stores a buffer 329, which is used by the wear detection module 310 and/or the movement detection module 320 during their respective movement and wear determinations. Each of the wear detection module 310 and the movement detection module 320 may be stand-alone components or a software module that is executed by the processor 321. The function of the wear detection module 310 and the movement detection module 320 are described in greater detail in relation to the method of FIG. 4.

As described above, the wearable sensor 300 can include a plurality of sensors. In some implementations, wear state is determined responsive to the wearable sensor's orientation and/or movement. In some of these implementations, the orientation is determined my measuring the gravitational acceleration along the x, y, and z axis. The accelerometers are configured to detect subtle movements, such as those that are difficult to spot with the naked eye. For example, the sensor 353 may detect movements that are caused by a user breathing or chewing. In some implementations, the sensors can detect orientation to within 1 degree, 0.5 degrees, or 0.01 degrees.

In some implementations, the processor 321 samples the signal generated by the sensors 353 at less than 10 Hz. For example, the processor 321 can samples the signal at about 1-5 Hz, about 3-7 Hz, or about 6-10 Hz. In some implementations, the processor 321 samples the signal as few as 1 sample per minute, 1 sample per 5 minutes, or 1 sample per 10 minutes. In yet other implementations, the processor 321 samples the signal above 10 Hz. For example, the processor 321 can sample the signal at about 10-50 Hz, about 50-100 Hz, about 100-200 Hz, about 200-1000 Hz.

FIG. 4 depicts a flow chart of a method 400 for determining the state of a wearable sensor. The method 400 includes sampling a motion signal (step 401) and determining an orientation (step 402). Responsive to sampling the motion signal, a movement is detected (step 403) and an indication of the detected motion is stored (step 404). Then, the state of the wearable sensor is determined (step 405).

As set forth above, the method 400 of determining the wearable sensor's state begins with the sampling of a motion signal (step 401). As described herein, the motion signal refers to any data signal that is generated by the one or more sensors of the wearable sensor. For example, a motion signal may be the acceleration of the wearable sensor that is measured by one or more accelerometers along one, two, or three axes. In some implementations, the sampling of the motion signal occurs in predetermined time steps. For example, a time steps include, but are not limited to, 30 seconds, 1 minute, and 5 minutes. In some implementations, the wearable sensor may record additional data to determine the wearable sensor's state. For example, the wearable sensor may record a RSSI value to determine approximate location of the wearable sensor.

In some implementations, the rate, the duration, and the duty cycle of sampling is responsive to the power requirements of the wearable sensor. For example, the wearable sensor may sample continuously at a rate of greater than 10 Hz in a “high” power state. In an example of a “low” power state, the wearable sensor may sample at 10 Hz for two seconds at the start of every time step. In another low power state example, the wearable sensor may sample at a low frequency. The low frequency may be only one sample per time step. In some implementations, sampling more than one sample per time step enables the movement detection module 320 to calculate an indication of acceleration magnitude (also referred to as intensity of the motion signal). The plurality of samples per time step may also be used to determine orientation. In some implementations, and as illustrated in relation to FIG. 2C, orientation can be determined with as little as one sample per time step.

At step 402, an orientation of the wearable sensor is determined. The orientation is determined by the above described movement detection module 320. The orientation can be indicative of a single orientation of the wearable sensor during the time step or the orientation can indicate a plurality of orientations for each time step. The orientation of the wearable sensor at each sampled data point may be determined using the below equations:

${Pitch} = {\arctan\left( \frac{G_{y}}{\sqrt{G_{x}^{2} + G_{z}^{2}}} \right)}$ ${Roll} = {\arctan\left( \frac{- G_{x}}{G_{z}} \right)}$

In the above equations, G_(i) represents the measured acceleration along the x, y, and z axes. In some implementations, the motion signal is filtered with a low pass filter to substantially isolate the acceleration due to gravity. The low pass filter can remove the component of the motion signal due to movement leaving substantially only the component caused by gravity.

In some implementations the measured acceleration may be used to determine both an orientation and an intensity. In some implementations, the intensity of the motion signal is the raw magnitude of the motion signal. In other implementations, intensity of the motion signal is a derived value that is calculated from the raw motion signal. For example, the intensity value can be the sum of the absolute value of the differential of the samples in the motion signal. More particularly, the intensity can be represented by the equation: I=Σ|x[n]−x[n+1]| where I is the intensity of the motion signal and x[n] is the sampled motion signal at sample n.

In a similar implementation, the intensity value can be the sum of the absolute value of the samples in the motion signal and a running average. More particularly, the intensity can be represented by the equation: I=Σ|x[n]−MA _(x)| where I is the intensity of the motion signal and x[n] is the sampled motion signal at sample n. MA_(x) is the moving average over the last x samples of the motion signal.

One of ordinary skill in the art will recognize, that this and other steps in the method 400 may be calculated “live” or at a later time. For example, in a “live” implementation, the samples from step 401 may be fed directly into the movement detection module 320 for substantially immediate analysis. In other implementations, the samples from step 401 may be stored in the storage device 328. The movement detection module 320 may retrieve the samples form the storage device 328 at a later time for analysis. In other implementations, as described above in relation to FIG. 1C, the wear detection module 310 and/or movement detection module 320 may be a component of one of the other devices. The data stored on the storage device 328 can be transferred to one of the other devices for the determination of wear state. For example, the data may be transferred to a server 106 for analysis.

At step 403, a detection of movement is made by the movement detection module 320. As described above, the sampling of the motion signal is binned into time steps. In some implementations, the detection of a movement is made for each of the time steps. For example, the sampled motion signal may be binned into one minute time steps, wherein the movement detection module 320 analyzes the one minute time steps to determine if a movement occurred during each time step. In this example, where the motion signal is binned into one minute time steps, if 60 minutes of data was sampled, then the movement detection module 320 would generate a total of sixty flags (or indications) of detected movement.

In some implementations, the method by which the wearable sensor detects movements is selected responsive to the type and amount of data recorded during the time step. For example, if the time step includes more than one data point, movement detection module 320 can use orientation and intensity to determine if movement occurred. If only one data sample was collected during the time step, the movement detection module 320 may only use orientation to determine if movement occurred.

In certain implementations, determining if a movement occurred is responsive to the orientation of the wearable sensor. For example, after the orientation has been obtained for a time step t_(n), the orientation is compared to an orientation from a previous time step t_(n-k), where k is a number of time steps in the past. For example, if k=1 the orientation at t_(n) is compared to the orientation at the time step immediately before time step t_(n). In some implementations, if the difference between the orientations is above a predetermined threshold then a flag is set that indicates that a movement occurred. In some implementations, the threshold is set a predetermined amount above the noise level generated by the sensor when the wearable sensor is at steady state. For example, the threshold may be set to value twice the height of the noise level. In some implementations, the threshold is set responsive to the desired level of sensitivity. For example, a lower threshold may be set if the wearer wishes to detect subtler movements. The threshold value can also be set responsive to the wearer's age, sex, health conditions, ethnicity, height, weight, the placement of wearable sensor, or any combination thereof.

In some implementations, determining if a movement occurred is also responsive to the intensity value(s) for each time step. For example, the movement detection module 320 may count the number of times the intensity rose above a threshold within the time step. If the number is above a second threshold, the movement detection module 320 may set a flag indicating that a movement occurred during the time step. In some implementations, the second threshold is set to reduce the number of false positives. For example, the second threshold may be set such that the wearable sensor does not classify bumping of an unworn wearable sensor as movement made by a wearer. For example, the second threshold may be set to correspond to the number of samples recorded in one second. In another implementation, a single intensity value may be calculated for the entire time step, such as using the above described equation for I. In these implementations, if the value of I is above a third threshold, then a flag to indicate movement is set. In some implementations, employing both intensity and orientation, one of or both the intensity calculations and the orientation calculations may have to register a movement before the movement detection module 320 sets a flag indicating movement for the time step.

At step 404, an indication of the detected movement is stored. As described in relation to step 403, the movement detection module 320 determines if movement occurred during each time step. When the determination of step 403 is complete, the movement detection module 320 stores the above described flag. In some implementations, the flag is stored into the buffer 329 within the storage device 328 of the wearable sensor. In some implementations, the buffer 329 stores between 2 and 5 flags, 5 and 20 flags, 20 and 50 flags, or more than 50 flags. The flags can indicate that no movement was detected, movements was detected, or the flags may indicate that a decision regarding movement could not be made for the particular time step.

At step 405, the wear detection module 310 determines if a wearer is wearing the wearable sensor. During step 405, the wear detection module 310 reads flags from the buffer 329. The determination of wear state may be responsive to N flags, where N is between 1 and the length of the buffer. For example, the determination as to if a wearable sensor is being worn during time step t_(n) may be responsive to the flag at t_(n) and the previous four flags. In this example, N=5.

In some implementations, the wear detection module 310 determines that the wearable sensor is being worn if a predetermined number of the last N flags indicate movement. For example, the wear detection module 310 may determine the wearable sensor is being worn if 4 of the last 5 flags indicate movement. In some implementations, the state determination made by the wear detection module 310 may be responsive to a predetermined pattern with in the last N flags. For example, the wear detection module 310 may require that at least M continuous flags indicate movement in order for the wear detection module 310 to determine the wearable sensor is in a wear state. 

What is claimed:
 1. A method for determining if a wearable device is worn by a wearer, the method comprising: sampling, by a wearable device comprising a motion sensor, for a predetermined time step a signal of the motion sensor indicating a motion of the wearable device; determining, by a processor of the wearable device, an orientation of the wearable device from the signal; detecting, by the processor of the wearable device, a movement of the wearable device based on a difference between the first orientation and at least one previously determined orientation; storing, by the processor of the wearable device, into an array of flags a flag that indicates detection of movement of the wearable device, wherein each flag in the array of flags indicates whether there has been detection of movement of the wearable device for each of the at least one previously predetermined time steps; and determining, by the processor of the wearable device, that the wearer is wearing the wearable device at the predetermined time step based on identifying a predetermined number of flags in the array of flags.
 2. The method of claim 1, wherein determining, by the processor of the wearable device, that the wearer is wearing the wearable device is responsive to identifying a continuous subset of flags in the array of flags that all indicate detection of movement.
 3. The method of claim 1, wherein detecting the movement of the wearable device further comprises: generating, by the wearable device, an intensity value for the predetermined time step by summing a plurality of samples of the signal over the predetermined time step; and determining, by the wearable device, if the intensity value is above a first threshold.
 4. The method of claim 3, wherein summing the samples of the movement signal further comprises summing, by the processor of the wearable device, the absolute value of the differences between adjacent samples in the plurality of samples.
 5. The method of claim 3, wherein summing the samples comprises summing, by the processor of the wearable device, the absolute value of each of the plurality of samples minus a running mean.
 6. The method of claim 1, wherein sampling further comprises sampling, by the processor of the wearable device, only one sample during the predetermined time step.
 7. The method of claim 1, wherein determining further comprises: counting, by the wearable device, a number of flags in the array of flags that have indicate detection of movement; and determining, by the wearable device, if the number of flags is above a second threshold.
 8. The method of claim 1, further comprising applying one of a band-pass filter, a low pass filter, or a highpass filter to the signal.
 9. The method of claim 1, wherein the motion sensor is one of an accelerometer, a gyroscope or a passive motion sensor.
 10. The method of claim 1, wherein sampling further comprises sampling, by the processor of the wearable device, for only a subset of time within the predetermined time step.
 11. The method of claim 1, wherein the orientation comprises one of the motion sensor's pitch, roll or yaw.
 12. The method of claim 1, wherein the first orientation is determined by filtering the signal and calculating the difference between the x, y, and z axis.
 13. The method of claim 1, wherein the determination that the wearer is wearing the wearable device is made by a second device in communication with the wearable device.
 14. A wearable device comprising: a processor in communication with a motion sensor and configured to: sample from the motion sensor for a predetermined time step a signal of the motion sensor indicating a motion of the wearable device; determine an orientation of the wearable device from the signal; detect a movement of the wearable device based on a difference between the first orientation and at least one previously determined orientation; store, responsive to the detection, into an array of flags a flag that indicates detection of movement of the wearable device, wherein each flag in the array of flags indicates whether there has been detection of movement of the wearable device for each of the at least one previously predetermined time steps; and determine that the wearer is wearing the wearable device at the predetermined time step based on identifying a predetermined number of flags in the array of flags.
 15. The wearable device of claim 14, wherein the processor is further configured to determine that the user is wearing the wearable responsive to identifying a continuous subset of flags in the array of flags that all indicate detection of movement.
 16. The wearable device of claim 14, wherein the processor is further configured to generate an intensity value for the time step by summing a plurality of samples of the signal over the predetermined time step; and determine if the intensity value is above a first threshold.
 17. The wearable device of claim 14, wherein the processor is further configured to sum the samples of the signal by summing the absolute value of the differences between adjacent samples in the plurality of samples.
 18. The wearable device of claim 14, wherein the processor is further configured to sample only one sample during the predetermined time step.
 19. The wearable device of claim 14, wherein the processor is further configured to determine that the user is wearing the wearable device by counting a number of flags in the array of flags that have indicate detection of movement and determining if the number of flags is above a second threshold.
 20. The wearable device of claim 14, further comprising a filter to apply one of a band-pass filter, a low pass filter or a highpass filter to the signal of the motion sensor.
 21. The wearable device of claim 14, wherein the motion sensor comprises one of an accelerometer, a gyroscope or a passive motion sensor.
 22. The wearable device of claim 14, wherein the processor is further configured to sample the signal with the motion sensor for only a subset of time within the predetermined time step.
 23. The wearable device of claim 14, wherein motion sensor is configured to measure at least two of a pitch, a roll, or a yaw. 